陈必焰, 戴吾蛟, 蔡昌盛, 夏朋飞. 层析反演与神经网络方法在电离层建模及预报中的应用[J]. 武汉大学学报 ( 信息科学版), 2012, 37(8): 972-975.
引用本文: 陈必焰, 戴吾蛟, 蔡昌盛, 夏朋飞. 层析反演与神经网络方法在电离层建模及预报中的应用[J]. 武汉大学学报 ( 信息科学版), 2012, 37(8): 972-975.
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CHEN Biyan, DAI Wujiao, CAI Changsheng, XIA Pengfei. Ionospheric Modeling and Forecasting Based on Tomographic and Neural Network Method[J]. Geomatics and Information Science of Wuhan University, 2012, 37(8): 972-975.
Citation: CHEN Biyan, DAI Wujiao, CAI Changsheng, XIA Pengfei. Ionospheric Modeling and Forecasting Based on Tomographic and Neural Network Method[J]. Geomatics and Information Science of Wuhan University, 2012, 37(8): 972-975.
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层析反演与神经网络方法在电离层建模及预报中的应用

Ionospheric Modeling and Forecasting Based on Tomographic and Neural Network Method

    摘要
  • 摘要: 将香港地区某天由电离层层析反演得到的电子密度值分成6组,利用神经网络方法对该6组数据分别进行了拟合建模及预报。实验结果表明,采用电离层层析技术并经神经网络模型预报得出的电子密度值精度明显高于由IRI2007模型提供的电子密度值,其预报的30min及60min的电子密度值精度可分别达到0.45TECU和1.34TECU。

     

    Abstract: The electron density values of one day in Hong Kong obtained by ionospheric tomographic are divided into 6 groups.Then,the 6 groups data are fitted modeling and forecasting by neural network method.The results show that the accuracy of electron density value forecasted by the tomographic and neural network model is significantly higher than the electron density value provided by IRI2007 model.The precision of the electron density forecast value within 30 minutes and 60 minutes can respectively arrive at 0.45 TECU and 1.34 TECU.

     

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